{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You are logged in as a guest:guest'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tradingeconomics as te\n",
    "te.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#plot a simple chart\n",
    "mydata = te.getHistoricalData(country = 'United states', indicator = 'Imports')\n",
    "\n",
    "plt.title(\"United states - Imports\")\n",
    "plt.grid(True)\n",
    "plt.ylabel(\"Indicator - Imports\")\n",
    "plt.xlabel(\"Historical\")\n",
    "\n",
    "plt.plot(mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "#plot a simple chart\n",
    "mydata = te.getHistoricalData(country = 'china', indicator = 'exports')\n",
    "\n",
    "plt.title(\"China - Exports\")\n",
    "plt.grid(True)\n",
    "plt.ylabel(\"Indicator - Exports\")\n",
    "plt.xlabel(\"Historical\")\n",
    "\n",
    "plt.plot(mydata)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'United States': {u'GDP': [[2016-12-31    18624.48\n",
      "2017-12-31    19390.60\n",
      "Name: 0, dtype: float64]], u'Population': [[2016-12-31    323.41\n",
      "2017-12-31    325.72\n",
      "Name: 0, dtype: float64]]}, u'Germany': {u'GDP': [[2015-12-31    3375.61\n",
      "2016-12-31    3477.80\n",
      "2017-12-31    3677.44\n",
      "Name: 0, dtype: float64]], u'Population': [[2015-12-31    81.20\n",
      "2016-12-31    82.18\n",
      "2017-12-31    82.52\n",
      "Name: 0, dtype: float64]]}}\n"
     ]
    }
   ],
   "source": [
    "mydata = te.getHistoricalData(country = ['United States', 'Germany'], indicator = ['Exports','Imports', 'GDP'], initDate= '1990-01-01', endDate= '2015-01-01')\n",
    "print(mydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>CalendarId</th>\n",
      "      <th>Date</th>\n",
      "      <th>Country</th>\n",
      "      <th>Category</th>\n",
      "      <th>Event</th>\n",
      "      <th>Reference</th>\n",
      "      <th>Unit</th>\n",
      "      <th>Source</th>\n",
      "      <th>Actual</th>\n",
      "      <th>Previous</th>\n",
      "      <th>Forecast</th>\n",
      "      <th>TEForecast</th>\n",
      "      <th>Importance</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>0</th>\n",
      "      <td>87213</td>\n",
      "      <td>2016-12-01T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Nov/26</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>268K</td>\n",
      "      <td>251K</td>\n",
      "      <td>253K</td>\n",
      "      <td>253.1K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>1</th>\n",
      "      <td>87229</td>\n",
      "      <td>2016-12-08T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Dec/03</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>258K</td>\n",
      "      <td>268K</td>\n",
      "      <td>258K</td>\n",
      "      <td>263K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>2</th>\n",
      "      <td>87240</td>\n",
      "      <td>2016-12-15T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Dec/10</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>254K</td>\n",
      "      <td>258K</td>\n",
      "      <td>255K</td>\n",
      "      <td>256K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>3</th>\n",
      "      <td>87249</td>\n",
      "      <td>2016-12-22T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Dec/17</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>275K</td>\n",
      "      <td>254K</td>\n",
      "      <td>256K</td>\n",
      "      <td>256K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>4</th>\n",
      "      <td>87260</td>\n",
      "      <td>2016-12-29T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Dec/24</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>265K</td>\n",
      "      <td>275K</td>\n",
      "      <td>264K</td>\n",
      "      <td>271K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>5</th>\n",
      "      <td>144647</td>\n",
      "      <td>2017-01-05T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Dec/31</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>235K</td>\n",
      "      <td>263K</td>\n",
      "      <td>240K</td>\n",
      "      <td>264K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>6</th>\n",
      "      <td>144663</td>\n",
      "      <td>2017-01-12T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Jan/07</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>247K</td>\n",
      "      <td>237K</td>\n",
      "      <td>245K</td>\n",
      "      <td>244K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>7</th>\n",
      "      <td>144673</td>\n",
      "      <td>2017-01-19T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Jan/14</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>234K</td>\n",
      "      <td>249K</td>\n",
      "      <td>240</td>\n",
      "      <td>247K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>8</th>\n",
      "      <td>144683</td>\n",
      "      <td>2017-01-26T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Jan/21</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>259K</td>\n",
      "      <td>237K</td>\n",
      "      <td>247K</td>\n",
      "      <td>247K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>9</th>\n",
      "      <td>144694</td>\n",
      "      <td>2017-02-02T13:30:00</td>\n",
      "      <td>United States</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Initial Jobless Claims</td>\n",
      "      <td>Jan/28</td>\n",
      "      <td></td>\n",
      "      <td>U.S. Department of Labor</td>\n",
      "      <td>246K</td>\n",
      "      <td>260K</td>\n",
      "      <td>250K</td>\n",
      "      <td>247K</td>\n",
      "      <td>2</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "mydata = te.getCalendarData(country = 'United States', category = 'Imports', initDate = '2018-03-12', endDate = '2019-03-12', output_type= 'df') \n",
    "df = pd.DataFrame(mydata)\n",
    "\n",
    "#If you want the code into an html format you can use in your html projects\n",
    "print(mydata.to_html())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "\n",
    "mydata = te.getCalendarData(country = 'United States', category = 'Imports', initDate = '2018-03-12', endDate = '2019-03-12', output_type= 'df') \n",
    "df = pd.DataFrame(mydata)\n",
    "#to get an csv file from the pandas DataFrame\n",
    "df.to_csv('mydata.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Date   Symbol                             Name   Actual  \\\n",
      "0  2019-04-08T00:00:00  ARCO:US                    Arcos Dorados     0.05   \n",
      "1  2019-04-08T00:00:00  BBVA:US    Banco Bilbaoizcaya Argentaria  0.14855   \n",
      "2  2019-04-08T00:00:00  BANR:US                           Banner     0.41   \n",
      "3  2019-04-08T00:00:00  BBVA:SM  Banco Bilbao Vizcaya Argentaria     0.26   \n",
      "4  2019-04-08T00:00:00   BRC:US                            Brady   0.2125   \n",
      "5  2019-04-08T00:00:00   BKW:AU                       BRICKWORKS     0.19   \n",
      "6  2019-04-08T00:00:00    DG:US                   Dollar General     0.32   \n",
      "7  2019-04-08T00:00:00   FOX:IT                        Fox Wizel   564.96   \n",
      "8  2019-04-08T00:00:00  GBCI:US                  Glacier Bancorp     0.26   \n",
      "9  2019-04-08T00:00:00   KAI:US                           Kadant     0.23   \n",
      "\n",
      "  Forecast FiscalTag FiscalReference CalendarReference        Country  \\\n",
      "0     None      None            None                 /      Argentina   \n",
      "1     None      None            None                 /          Spain   \n",
      "2     None      None            None                 /  United States   \n",
      "3     None      None            None                 /          Spain   \n",
      "4     None      None            None                 /  United States   \n",
      "5     None      None            None                 /      Australia   \n",
      "6     None      None            None                 /  United States   \n",
      "7     None      None            None                 /         Israel   \n",
      "8     None      None            None                 /  United States   \n",
      "9     None      None            None                 /  United States   \n",
      "\n",
      "  Currency           LastUpdate  \n",
      "0     None  2019-03-31T00:11:00  \n",
      "1     None  2019-03-31T00:11:00  \n",
      "2     None  2019-03-31T00:11:00  \n",
      "3     None  2019-03-31T00:11:00  \n",
      "4     None  2019-03-31T00:11:00  \n",
      "5     None  2019-03-31T00:11:00  \n",
      "6     None  2019-03-31T00:11:00  \n",
      "7     None  2019-03-31T00:11:00  \n",
      "8     None  2019-03-31T00:11:00  \n",
      "9     None  2019-03-31T00:11:00  \n"
     ]
    }
   ],
   "source": [
    "mydata = te.getEarnings(output_type= 'df')\n",
    "print(mydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'United States': {u'GDP': [{'q1': 20220.0, 'q3': 20220.0, 'q2': 20220.0, 'YearEnd2': 20700.0, 'YearEnd3': 20700.0, 'YearEnd': 20220.0, 'q1_date': u'2019-06-30T00:00:00', 'LatestValue': 19390.6, 'q2_date': u'2019-09-30T00:00:00', 'q4_date': u'2020-03-31T00:00:00', 'LatestValueDate': u'2017-12-31T00:00:00', 'q3_date': u'2019-12-31T00:00:00', 'q4': 20700.0}]}}\n"
     ]
    }
   ],
   "source": [
    "mydata = te.getForecastData(country = 'United States', indicator = 'Imports')\n",
    "print(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id                           title                     date  \\\n",
      "0  20580  US Jobless Claims Rise to 265K  2016-03-17T12:37:48.177   \n",
      "\n",
      "                                         description  \\\n",
      "0  The number of Americans filing for unemploymen...   \n",
      "\n",
      "                                             content        country  \\\n",
      "0  <div>\\n\\tThe 4-week moving average was 268,000...  United States   \n",
      "\n",
      "                 category  symbol                           url  \n",
      "0  Initial Jobless Claims  IJCUSA  /articles/03172016123748.htm  \n"
     ]
    }
   ],
   "source": [
    "mydata = te.getArticleId(id = '20580', output_type = None)\n",
    "\n",
    "print(mydata)"
   ]
  }
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